{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "031520007零零七\n"
     ]
    }
   ],
   "source": [
    "print('031520007零零七')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd \n",
    "#import warnings\n",
    "#warnings.filterwarnings(\"ignore\")#忽略警告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 12)\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"titanic_train.csv\")\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
       "0         0       3    male  22.0      1      0         A/5 21171   7.2500   \n",
       "1         1       1  female  38.0      1      0          PC 17599  71.2833   \n",
       "2         1       3  female  26.0      0      0  STON/O2. 3101282   7.9250   \n",
       "3         1       1  female  35.0      1      0            113803  53.1000   \n",
       "4         0       3    male  35.0      0      0            373450   8.0500   \n",
       "\n",
       "  Cabin Embarked  \n",
       "0   NaN        S  \n",
       "1   C85        C  \n",
       "2   NaN        S  \n",
       "3  C123        S  \n",
       "4   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(columns=['Name'],inplace=True)\n",
    "data.drop(columns=['PassengerId'],inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Ticket        0\n",
       "Fare          0\n",
       "Cabin       687\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.42,  0.67,  0.75,  0.83,  0.92,  1.  ,  2.  ,  3.  ,  4.  ,\n",
       "        5.  ,  6.  ,  7.  ,  8.  ,  9.  , 10.  , 11.  , 12.  , 13.  ,\n",
       "       14.  , 14.5 , 15.  , 16.  , 17.  , 18.  , 19.  , 20.  , 20.5 ,\n",
       "       21.  , 22.  , 23.  , 23.5 , 24.  , 24.5 , 25.  , 26.  , 27.  ,\n",
       "       28.  , 28.5 , 29.  , 30.  , 30.5 , 31.  , 32.  , 32.5 , 33.  ,\n",
       "       34.  , 34.5 , 35.  , 36.  , 36.5 , 37.  , 38.  , 39.  , 40.  ,\n",
       "       40.5 , 41.  , 42.  , 43.  , 44.  , 45.  , 45.5 , 46.  , 47.  ,\n",
       "       48.  , 49.  , 50.  , 51.  , 52.  , 53.  , 54.  , 55.  , 55.5 ,\n",
       "       56.  , 57.  , 58.  , 59.  , 60.  , 61.  , 62.  , 63.  , 64.  ,\n",
       "       65.  , 66.  , 70.  , 70.5 , 71.  , 74.  , 80.  ,   nan])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.unique(data[\"Age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "a2=data[\"Age\"].mean()\n",
    "data[\"Age\"]=data[\"Age\"].fillna(a2)\n",
    "data['Embarked'] = data['Embarked'].replace(np.nan, 'AA', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Ticket     Fare Embarked\n",
       "0         0       3    male  22.0      1      0  A/5 21171   7.2500        S\n",
       "1         1       1  female  38.0      1      0   PC 17599  71.2833        C"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(columns=['Cabin'],inplace=True)\n",
    "#data.drop(columns=['Ticket'],inplace=True)\n",
    "data[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Ticket      0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Ticket     Fare Embarked\n",
       "0         0       3    1  22.0      1      0  A/5 21171   7.2500        S\n",
       "1         1       1    0  38.0      1      0   PC 17599  71.2833        C"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "class_le = LabelEncoder()\n",
    "data['Sex'] = class_le.fit_transform(data['Sex'])\n",
    "data[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>523</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>596</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch  Ticket     Fare Embarked\n",
       "0         0       3    1  22.0      1      0     523   7.2500        S\n",
       "1         1       1    0  38.0      1      0     596  71.2833        C"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "class_mapping = {label: idx for idx, label in enumerate(np.unique(data['Ticket']))}\n",
    "class_mapping\n",
    "data['Ticket']=data['Ticket'].map(class_mapping)\n",
    "data[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data=pandas.get_dummies(data['Embarked'])\n",
    "data2=pd.get_dummies(data['Embarked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AA</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      AA      C      Q      S\n",
       "0  False  False  False   True\n",
       "1  False   True  False  False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>523</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>596</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch  Ticket     Fare Embarked\n",
       "0         0       3    1  22.0      1      0     523   7.2500        S\n",
       "1         1       1    0  38.0      1      0     596  71.2833        C"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>AA</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>523</td>\n",
       "      <td>7.25</td>\n",
       "      <td>S</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch  Ticket  Fare Embarked     AA  \\\n",
       "0         0       3    1  22.0      1      0     523  7.25        S  False   \n",
       "\n",
       "       C      Q     S  \n",
       "0  False  False  True  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data.drop(columns=['Embarked'],inplace=True)\n",
    "datanew=pd.concat([data,data2],axis=1)\n",
    "#datanew.to_csv('titanicx.csv')\n",
    "datanew[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>AA</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>523</td>\n",
       "      <td>7.25</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch  Ticket  Fare     AA      C  \\\n",
       "0         0       3    1  22.0      1      0     523  7.25  False  False   \n",
       "\n",
       "       Q     S  \n",
       "0  False  True  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datanew.drop(columns=['Embarked'],inplace=True)\n",
    "datanew[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>0.647587</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>338.528620</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>0.477990</td>\n",
       "      <td>13.002015</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>200.850657</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>158.500000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>337.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>519.500000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>680.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Survived      Pclass         Sex         Age       SibSp       Parch  \\\n",
       "count  891.000000  891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean     0.383838    2.308642    0.647587   29.699118    0.523008    0.381594   \n",
       "std      0.486592    0.836071    0.477990   13.002015    1.102743    0.806057   \n",
       "min      0.000000    1.000000    0.000000    0.420000    0.000000    0.000000   \n",
       "25%      0.000000    2.000000    0.000000   22.000000    0.000000    0.000000   \n",
       "50%      0.000000    3.000000    1.000000   29.699118    0.000000    0.000000   \n",
       "75%      1.000000    3.000000    1.000000   35.000000    1.000000    0.000000   \n",
       "max      1.000000    3.000000    1.000000   80.000000    8.000000    6.000000   \n",
       "\n",
       "           Ticket        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean   338.528620   32.204208  \n",
       "std    200.850657   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%    158.500000    7.910400  \n",
       "50%    337.000000   14.454200  \n",
       "75%    519.500000   31.000000  \n",
       "max    680.000000  512.329200  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datanew.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datanew[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(\n",
    "    by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived\n",
       "Sex          \n",
       "0    0.742038\n",
       "1    0.188908"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datanew[[\"Sex\", \"Survived\"]].groupby(['Sex']).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\student\\anaconda3\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e51efa9810>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "g = sns.FacetGrid(datanew, col='Survived')\n",
    "g.map(plt.hist, 'Age', bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = datanew.corr()\n",
    "\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "# build figure\n",
    "f, ax = plt.subplots(figsize=(25, 15))\n",
    "# change x- and y-label size\n",
    "ax.tick_params(axis='both', which='major', labelsize=15)\n",
    "# plot mast\n",
    "sns.heatmap(corr, mask=mask, cmap=\"coolwarm\", center=0, square=True, linewidths=1, linecolor=\"#424949\", annot=True, \n",
    "                cbar_kws={\"shrink\": 0.6}).set_title('Pairwise correlation', fontsize=\"28\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=datanew['Survived']\n",
    "x=datanew.iloc[:,1:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>AA</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>523</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>596</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex   Age  SibSp  Parch  Ticket     Fare     AA      C      Q  \\\n",
       "0       3    1  22.0      1      0     523   7.2500  False  False  False   \n",
       "1       1    0  38.0      1      0     596  71.2833  False   True  False   \n",
       "\n",
       "       S  \n",
       "0   True  \n",
       "1  False  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "平均精度： 0.7979473981545414\n",
      "[0.82681564 0.76966292 0.7752809  0.7752809  0.84269663]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets, metrics\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "standardizer = StandardScaler()\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "logit = LogisticRegression()\n",
    "pipeline= make_pipeline(standardizer,LogisticRegression())\n",
    "kf = KFold(n_splits=5, shuffle=True)\n",
    "cv_results = cross_val_score(pipeline,  x,  y, # target vector\n",
    "                             cv=kf,  n_jobs=1) # use all CPU cores\n",
    "print( '平均精度：' ,cv_results.mean())\n",
    "print(cv_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.805970\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\student\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y,test_size=0.3,  random_state=123)\n",
    "logit = LogisticRegression()\n",
    "logit.fit(X_train, y_train)\n",
    "print('Accuracy: %.6f' % logit.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.850746\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "forest=RandomForestClassifier()\n",
    "forest.fit(X_train, y_train)\n",
    "print('Accuracy: %.6f' % forest.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.854478\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier#集成梯度提升决策树\n",
    "grb=GradientBoostingClassifier(learning_rate=0.1).fit(X_train, y_train)\n",
    "print('Accuracy: %.6f' % grb.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.761\n",
      "Accuracy on test set: 0.761\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier  #####神经网络方法\n",
    "mlp = MLPClassifier(random_state=0,max_iter=3000)\n",
    "mlp.fit(X_train, y_train)\n",
    "print(\"Accuracy on training set: {:.3f}\".format(mlp.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(mlp.score(X_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.832090\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier(criterion='gini', \n",
    "                              max_depth=4, \n",
    "                              random_state=1)\n",
    "tree.fit(X_train, y_train)\n",
    "print('Accuracy: %.6f' % tree.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.708955\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC(kernel='rbf', random_state=1, gamma=0.01, C=1)\n",
    "svm.fit(X_train, y_train)\n",
    "print('Accuracy: %.6f' % svm.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.712687\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=5,  metric='minkowski')\n",
    "knn.fit(X_train, y_train)\n",
    "print('Accuracy: %.6f' % knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8202451612903225 \n",
      "\n",
      "{'learning_rate': 0.1}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "#grid= GridSearchCV(grb, param_grid, cv=5,scoring='accuracy')\n",
    "Cs = [0.0001, 0.001, 0.01,0.1,0.3,0.6,1.0]\n",
    "aram_test2= dict( learning_rate= Cs)\n",
    "gsearch2= GridSearchCV(\n",
    "    estimator =GradientBoostingClassifier(), param_grid =aram_test2,scoring='accuracy') #accuracy#roc_auc\n",
    "gsearch2.fit(X_train, y_train)  \n",
    "#gsearch1.cv_results_\n",
    "print(gsearch2.best_score_,'\\n')\n",
    "print(gsearch2.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8544776119402985"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch2.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9165329052969502"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch2.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8507462686567164"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator =GradientBoostingClassifier(learning_rate= 0.3)\n",
    "estimator.fit(X_train, y_train)  \n",
    "estimator.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9903691813804173"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8058193548387098 \n",
      "\n",
      "{'n_estimators': 390}\n"
     ]
    }
   ],
   "source": [
    "param_test1 ={'n_estimators':range(50,401,20)}  \n",
    "gsearch1= GridSearchCV(\n",
    "    estimator =RandomForestClassifier(), \n",
    "    param_grid =param_test1,scoring='accuracy')  #accuracy#roc_auc\n",
    "gsearch1.fit(X_train, y_train)  \n",
    "#gsearch1.cv_results_\n",
    "print(gsearch1.best_score_,'\\n')\n",
    "print(gsearch1.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[151  19]\n",
      " [ 20  78]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8544776119402985"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "estimator =RandomForestClassifier(n_estimators=60)\n",
    "estimator.fit(X_train, y_train)\n",
    "y_pred =estimator.predict(X_test)\n",
    "confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)\n",
    "print(confmat)\n",
    "estimator.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e52074ebd0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve\n",
    "forest=RandomForestClassifier(n_estimators=80)\n",
    "forest.fit(X_train, y_train)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, forest.predict_proba(X_test)[:,1])\n",
    "x=np.linspace(0,1,100)\n",
    "plt.plot(x,x, label=\"random guessing\")\n",
    "plt.plot(fpr, tpr, label=\"ROC Curve\")\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR (recall)\")\n",
    "close_zero = np.argmin(np.abs(thresholds))\n",
    "#plt.plot(fpr[close_zero], tpr[close_zero], 'o', markersize=10,\n",
    " #        label=\"threshold zero\", fillstyle=\"none\", c='k', mew=2)\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC for Random Forest: 0.875\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "rf_auc = roc_auc_score(y_test, forest.predict_proba(X_test)[:, 1])\n",
    "print(\"AUC for Random Forest: {:.3f}\".format(rf_auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
